Construction Of Knowledge Base Research Articles

CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models

Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This method addresses common LLM limitations, including outdated information and the tendency to produce inaccurate “hallucinated” content. However, evaluating RAG systems is a challenge. Most benchmarks focus primarily on question answering applications, neglecting other potential scenarios where RAG could be beneficial. Accordingly, in the experiments, these benchmarks often assess only the LLM components of the RAG pipeline or the retriever in knowledge-intensive scenarios, overlooking the impact of external knowledge base construction and the retrieval component on the entire RAG pipeline in non-knowledge-intensive scenarios. To address these issues, this paper constructs a large-scale and more comprehensive benchmark, and evaluates all the components of RAG systems in various RAG application scenarios. Specifically, we refer to the CRUD actions that describe interactions between users and knowledge bases, and also categorize the range of RAG applications into four distinct types–Create, Read, Update, and Delete (CRUD). “Create” refers to scenarios requiring the generation of original, varied content. “Read” involves responding to intricate questions in knowledge-intensive situations. “Update” focuses on revising and rectifying inaccuracies or inconsistencies in pre-existing texts. “Delete” pertains to the task of summarizing extensive texts into more concise forms. For each of these CRUD categories, we have developed different datasets to evaluate the performance of RAG systems. We also analyze the effects of various components of the RAG system, such as the retriever, context length, knowledge base construction, and LLM. Finally, we provide useful insights for optimizing the RAG technology for different scenarios 1 .

MOTIVAÇÃO NO PROCESSO DE ENSINO- APRENDIZAGEM: UM OLHAR SOBRE FATORES MOTIVACIONAIS E DESMOTIVACIONAIS NA EDUCAÇÃO DA SOCIEDADE BRASILEIRA

Studies in the field of Pedagogy and Psychology highlight numerous theories about motivation in the teaching- learning field, indicating them as an important resource to be observed. The interest in the subject is vast in the academic literature and offers significant contributions, mainly in the sense of collaborating with the different pedagogical proposals for the realization of learning and, in this way, favoring the apprehension of contents in the construction of a solid knowledge base. This article presents a bibliographic research, with the objective of observing the studies on motivation carried out by Abraham Maslow and reflecting on which points favor or hinder/demotivate the teacher and the students' learning process. The results indicate that motivational states can contribute positively to education professionals, in order to seek in theory a support in understanding and a starting point for changing educational attitudes in order to equate and create strategies for improvement in the teaching- learning process. , in addition to transforming the classroom into a fertile environment for innovations.

MED-ChatGPT CoPilot: a ChatGPT medical assistant for case mining and adjunctive therapy.

The large-scale language model, GPT-4-1106-preview, supports text of up to 128 k characters, which has enhanced the capability of processing vast quantities of text. This model can perform efficient and accurate text data mining without the need for retraining, aided by prompt engineering. The research approach includes prompt engineering and text vectorization processing. In this study, prompt engineering is applied to assist ChatGPT in text mining. Subsequently, the mined results are vectorized and incorporated into a local knowledge base. After cleansing 306 medical papers, data extraction was performed using ChatGPT. Following a validation and filtering process, 241 medical case data entries were obtained, leading to the construction of a local medical knowledge base. Additionally, drawing upon the Langchain framework and utilizing the local knowledge base in conjunction with ChatGPT, we successfully developed a fast and reliable chatbot. This chatbot is capable of providing recommended diagnostic and treatment information for various diseases. The performance of the designed ChatGPT model, which was enhanced by data from the local knowledge base, exceeded that of the original model by 7.90% on a set of medical questions. ChatGPT, assisted by prompt engineering, demonstrates effective data mining capabilities for large-scale medical texts. In the future, we plan to incorporate a richer array of medical case data, expand the scale of the knowledge base, and enhance ChatGPT's performance in the medical field.

Ontology construction of traditional Chinese medicine diagnosis and treatment for myopia

Objective: To keep up with the progress of treatment for myopia in traditional Chinese medicine (TCM) through literature research, so as to determine data source of TCM diagnosis and treatment for myopia, from which entities are extracted for integration of relevant knowledge, and further formal expression and visualization of knowledge. Methods: Literature research and comparative analysis were used to determine the data source, and the seven-step method of constructing ontology was referred to for extraction of TCM diagnosis and treatment entities, which then was terminalized for development of classes and class hierarchy, and class properties, as well as creation of instances before consistency test of the ontology. Results: The ontology of TCM diagnosis and treatment for myopia includes 26 classes and 124 instances, 9 object properties and 1 data property. Conclusion: The combination of ontology technology and TCM diagnosis and treatment knowledge of myopia contributes to the construction of knowledge base of TCM diagnosis and treatment ontology for myopia, which realizes formal expression and visualization of relevant TCM knowledge, lays a foundation for the construction of TCM top-level ontology and ophthalmology ontology, and facilitates knowledge reusing and sharing.

Research on Construction of Knowledge Base for MRI Equipment Maintenance Based on Knowledge Graph

To improve the level of modern hospital management and establish a scientific and effective medical equipment maintenance guarantee system, this paper proposes the construction of a dynamic interactive fault repair knowledge base system for magnetic resonance imaging (MRI) equipment based on knowledge graph technology. This innovative application of intelligent management for large medical equipment involves collecting and organizing actual maintenance data. The paper constructs the medical equipment maintenance knowledge base, with MRI equipment as a case study, and initially implements functions such as fault information retrieval, technical guidance, and training and learning for MRI equipment. These functions aim to provide an objective decision-making basis for the maintenance and management of large medical equipment, enhance the efficiency of fault repair and maintenance, and elevate the construction level of intelligent hospitals.

Method of automated knowledge base construction for вusiness process management information systems

The object of research is the process of automated construction and enrichment of a knowledge base. An approach has been defined to address the problem of automated knowledge base construction in information system development projects. Research has been conducted on models and methods for the automatic construction of a knowledge base for process management information systems. It has been determined that finite predicates, as well as predicate models describing IT project artifacts, are suitable as the mathematical apparatus. The method for automated construction and enrichment of a knowledge base for process management information systems has been improved based on modified predicate models. The modification involves adding new predicates and aligning event logs with classes of artifacts and their properties. The feasibility of applying the proposed method in decision support tasks under intrusion detection conditions for processes in computer systems has been verified. The architecture and description of the main characteristics of the automated knowledge base construction system for decision-making tasks in IT enterprises, are provided. The proposed solutions have been tested during the automated construction of a knowledge base in an IT project. Advantages and disadvantages of the method were identified during the automated construction of a production knowledge base. Further development of the modified method may involve analyzing the success of predictions made using the knowledge base and adjusting the initial set of predicates based on the results of this analysis.

Substitution of a Traditional Face-to-Face Workshop With Virtual Escape Room in Higher Education: A Cost-Effectiveness Analysis.

Online education games are gaining ground in health profession education, yet there is limited literature on its costs. This study is an economic evaluation of the substitution of a face-to-face (F2F) workshop with an online escape room game teaching the same content. A traditional F2F workshop on hepatitis management was conducted with 364 students in 2021 and was compared with a virtual self-run escape room game called Hepatitiscape™, which was used by 417 students in 2022. The outcomes were final examination and objective structured clinical examination (OSCE) scores for hepatitis stations. An incremental cost-effectiveness ratio was used to compare costs and outcomes. Student perceptions of the delivery of Hepatitiscape™ were also captured using an online questionnaire. Delivering the hepatitis case workshop via Hepatitiscape™ yielded an additional 4.77% increase in the final examination score and a 21.04% increase in the OSCE score at an additional cost of AUD $4212 in the first year compared with F2F delivery. This equated to an incremental cost-effectiveness ratio of AUD 883 per additional score of final examination and AUD 200 per additional score of OSCE for hepatitis stations. Hepatitiscape™ became cost saving from the second year onward. Student perception data revealed their recall of content was higher owing to the iterative design of the gaming elements. Hepatitiscape™ is likely to be a cost-effective strategy to deliver workshops that are routinely delivered F2F to test knowledge-based constructs. In addition, virtual gaming has a logistical advantage over F2F delivery in that it enhances student participation from remote locations and allows for better control and flexibility of content delivery with increasing or decreasing cohort sizes, and can have potential long-term sustainable savings.

The formation of attendee loyalty at cultural festivals. What role for knowledge and authenticity?

Scholars have only recently been focusing on the study of knowledge in the formation of loyalty in cultural festivals and much remains to be understood regarding this. The present study adopts a managerial standpoint and develops a knowledge-based model of attendee loyalty in cultural festivals in which the role of authenticity is also evaluated. It considers two knowledge-based constructs, prior knowledge and acquired knowledge, referring to the cultures and the traditions evoked during the festival, and intercepts their role with respect to loyalty also in conjunction with authenticity. Based on data collected from 306 attendees at a cultural festival organized annually in South Italy, a co-variance-based SEM was performed. Results highlight that prior and acquired knowledge positively influence loyalty. Perceived authenticity acts as a mediator in these relationships. These findings provide theoretical and practical implications for researchers and festival organizers.

Information Resilience in Networks: An Analysis of a National Security Legislation Evidence Base

Governance theory emphasises that networks are pivotal to societal governance and related steering mechanisms. One important means of steering is legislation, the evidence base of which is built on the interaction between social actors and the expert hearings related to the legislative process. This research uses network analysis to examine the construction of a legislative knowledge base and the information resilience displayed in the related networks. The data are derived from experts (n=440) appearing in committee hearings on five proposed acts of parliament. The results show that the expert consultations behind the legislation are official-oriented and illuminate the limited information produced by the private sector and a narrow view of scientific expertise. The network reveals epistemic authorities – mainly representing the security cluster – acting as gatekeepers. A more systemic approach is needed to build an evidence base encompassing the views on societal phenomena derived from different disciplines.

Exploring the Role of Self-Adaptive Feature Words in Relation Quintuple Extraction for Scientific Literature

Extracting relation quintuple and feature words from unstructured text is a prelude to the construction of the scientific knowledge base. At present, the prior works use explicit clues between entities to study this task but ignore the use and the association of the feature words. In this work, we propose a new method to generate self-adaptive feature words from the original text for every single sample. These words can add additional correlation information to the knowledge graph. We allow the model to generate a new word representation and apply it to the original sentence to judge the relation type and locate the head and tail of the relation quintuple. Compared with the previous works, the feature words increase the flexibility of relying on information and improve the explanatory ability. Extensive experiments on scientific field datasets illustrate that the self-adaptive feature words method (SAFW) is good at ferreting out the unique feature words and obtaining the core part for the quintuple. It achieves good performance on four public datasets and obtains a markable performance improvement compared with other baselines.

Research on the Construction Method of Knowledge Base of Automatic Question Answering System for Labor Law

At present, with the rapid development of China's economy, labor relations in society are becoming more and more tense. Therefore, it is urgent and necessary to develop an automatic question and answer system for labor law to solve problems in legislation, law enforcement, justice, rights protection, etc., and the construction of labor law knowledge base is the core system of the automatic question and answer system of labor law. With the development and training of large AI models, the automatic question answering system is becoming more and more mature, and this paper will use FastGPT to build a local knowledge base and build a knowledge base of the automatic question answering system for labor law.

Joint extraction of wheat germplasm information entity relationship based on deep character and word fusion

The verified text data of wheat varieties is an important component of wheat germplasm information. To automatically obtain a structured description of the phenotypic and genetic characteristics of wheat varieties, the aim at solve the issues of fuzzy entity boundaries and overlapping relationships in unstructured wheat variety approval data, WGIE-DCWF (joint extraction model of wheat germplasm information entity relationship based on deep character and word fusion) was proposed. The encoding layer of the model deeply fused word semantic information and character information using the Transformer encoder of BERT. This allowed for the cascading fusion of contextual semantic feature information to achieve rich character vector representation and improve the recognition ability of entity features. The triple extraction layer of the model established a cascading pointer network, extracted the head entity, extracted the tail entity according to the relationship category, and decoded the output triplet. This approach improved the model’s capability to extract overlapping relationships. The experimental results demonstrated that the WGIE-DCWF model performed exceptionally well on both the WGD (wheat germplasm dataset) and the public dataset DuIE. The WGIE-DCWF model not only achieved high performance on the evaluation datasets but also demonstrated good generalization. This provided valuable technical support for the construction of a wheat germplasm information knowledge base and is of great significance for wheat breeding, genetic research, cultivation management, and agricultural production.

Product Improvement Using Knowledge Mining and Effect Analogy

Different from new product development, design improvement aims to solve the problems of existing products. Although design knowledge and effect tools have been applied in product improvement, the existing methods for design improvement are limited in their specific application areas. A general method of product improvement is proposed in this paper using the knowledge mining and effect analogy. The length–time dimension is introduced to link the problem analysis and problem-solving for the first time. This method includes the effect knowledge base construction, length–time dimension extraction, effect retrieval, effect ranking, analogy object selection, and effect structure mapping. This method integrates a variety of algorithms and software tools in design knowledge mining to improve the efficiency of the effect analogy for product improvement. Through the comparative analysis of three effect retrieval methods and design improvement of a button battery ring device, the superiority and feasibility of the proposed method are verified.

Quality Evaluation of Triples in Knowledge Graph by Incorporating Internal With External Consistency.

The evaluation of knowledge quality (KQ) in multisource knowledge graphs (KGs) is an essential step for many applications, such as fragmented knowledge fusion and knowledge base construction. Many existing quality evaluation methods for multisource knowledge are based on validation from high-quality knowledge bases or statistical analysis of knowledge related to a specific fact from multiple sources, named external consistency (EC)-based methods. However, high-quality KGs are difficult to obtain, and there might exist incorrect knowledge in multisource KGs interfering with KQ evaluation. To address the issue, this article refers to the internal structure of a KG to evaluate the degree to which the contained triples conform to the overall semantic pattern of the KG, such as KG embedding and logic inference-based approaches, defined as internal consistency (IC) evaluation. The IC is integrated with the EC to identify possible incorrect triples and reduce their influences on the KQ evaluation, thus alleviating the interference of incorrect knowledge. The proposed method is verified with multiple datasets, and the results demonstrate that the proposed method could significantly reduce wrong evaluations caused by incorrect knowledge and effectively improve the quality evaluation of triples.

A novel fracture-cavity reservoir outcrop geological knowledge base construction method considering parameter collection and processing, mutual transformation of data-knowledge, application and update

This study endeavors to formulate a comprehensive methodology for establishing a Geological Knowledge Base (GKB) tailored to fracture-cavity reservoir outcrops within the North Tarim Basin. The acquisition of quantitative geological parameters was accomplished through diverse means such as outcrop observations, thin section studies, unmanned aerial vehicle scanning, and high-resolution cameras. Subsequently, a three-dimensional digital outcrop model was generated, and the parameters were standardized. An assessment of traditional geological knowledge was conducted to delineate the knowledge framework, content, and system of the GKB. The basic parameter knowledge was extracted using multiscale fine characterization techniques, including core statistics, field observations, and microscopic thin section analysis. Key mechanism knowledge was identified by integrating trace elements from filling, isotope geochemical tests, and water-rock simulation experiments. Significant representational knowledge was then extracted by employing various methods such as multiple linear regression, neural network technology, and discriminant classification. Subsequently, an analogy study was performed on the karst fracture-cavity system (KFCS) in both outcrop and underground reservoir settings. The results underscored several key findings: (1) Utilization of a diverse range of techniques, including outcrop observations, core statistics, unmanned aerial vehicle scanning, high-resolution cameras, thin section analysis, and electron scanning imaging, enabled the acquisition and standardization of data. This facilitated effective management and integration of geological parameter data from multiple sources and scales. (2) The GKB for fracture-cavity reservoir outcrops, encompassing basic parameter knowledge, key mechanism knowledge, and significant representational knowledge, provides robust data support and systematic geological insights for the intricate and in-depth examination of the genetic mechanisms of fracture-cavity reservoirs. (3) The developmental characteristics of fracture-cavities in karst outcrops offer effective, efficient, and accurate guidance for fracture-cavity research in underground karst reservoirs. The outlined construction method of the outcrop geological knowledge base is applicable to various fracture-cavity reservoirs in different layers and regions worldwide.

Promoting high-quality development of acupuncture and moxibustion.

In the past 20 years, the acupuncture-moxibustion discipline has made a great progress in clinical research, method construction, standard formulation, guideline promotion, basic theory and key scientific issue research. Internationally, the development of acupuncture and moxibustion has gradually begun to pay more attention to the basic issues of the discipline itself from focusing on clinical evidence. The National Institute of Health of USA pays close attention to the construction of acupoint knowledge base and database and to the transformation of peripheral nerve stimulation techniques, which brings forth opportunities and challenges for the development of acupuncture-moxibustion discipline. In the present paper, we analyze the shortcomings of the current development of acupuncture and moxibustion, put forward some strategies for high-quality development in the future, and sort out the basic scientific issues to form an academic consensus. We should employ modern scientific language to express the scientific connotations of the basic theory of acupuncture and moxibustion, and build an open and self-consistent modern theoretical system. In addition, we also should attract more multidisciplinary talents to harmoniously and assiduously work together, insist on continuous innovation to open up a new situation in the transformation of basic scientific research achievements, and establish a new theoretical system of "somato-medicine" represented by acupuncture and moxibustion. In this way, we will guide the acupuncture-moxibustion discipline to make an original contribution to the modern life science and future medicine.

Knowledge reasoning in power grid infrastructure projects based on deep multi-view graph convolutional network

With the rapid development of power grid infrastructure, especially the increasing number of ultra-high voltage (UHV) projects, knowledge extracted from historical engineering data is collected and can be potentially used to assist in the review of power transmission and transformation projects. However, conventional knowledge modeling and knowledge reasoning methods cannot meet the current needs of power grid construction. In this paper, considering the more supernumerary and distinctive information brought by multi-view data which could be beneficial for feature representation and knowledge reasoning from the constructed knowledge base, a multi-view graph convolutional network (GCN) based on knowledge graph is proposed to make classification for power grid infrastructure projects. Specifically, several views are constructed based on attribute information of a knowledge graph. In addition, a Haar convolution-based pooling mechanism is employed to capture the structural features represented by a chain of subgraphs. And then an aggregator that combines both attribute and structural information is used to classify UHV projects. Results from both UHV and NCI-1 datasets indicate that our proposed method is more has higher accuracy and generalization ability.

Preliminary Exploration of the Construction of a Basic Spanish Grammar Knowledge Base with Bi-Directional Linking Note-Taking Software

Preliminary Exploration of the Construction of a Basic Spanish Grammar Knowledge Base with Bi-Directional Linking Note-Taking Software

MatSciRE: Leveraging pointer networks to automate entity and relation extraction for material science knowledge-base construction

Material science literature is a rich source of factual information about various categories of entities (like materials and compositions) and various relations between these entities, such as conductivity, voltage, etc. Automatically extracting this information to generate a material science knowledge base is a challenging task. In this paper, we propose MatSciRE (Material Science Relation Extractor), a Pointer Network-based encoder–decoder framework, to jointly extract entities and relations from material science articles as a triplet (entity1,relation,entity2). Specifically, we target the battery materials and identify five relations to work on — conductivity, coulombic efficiency, capacity, voltage, and energy. Our proposed approach achieved a much better F1-score (0.771) than a previous attempt using ChemDataExtractor (0.716). The overall graphical framework of MatSciRE is shown in Fig. 1. The material information is extracted from material science literature in the form of entity–relation triplets using MatSciRE.

An Artificial Intelligence-Based Model for Knowledge Evaluation and Integration in Public Organizations

In the construction of knowledge bases, it is very important to evaluate the quality of the knowledge entered into them. This is exacerbated in public administrations, where knowledge should be oriented towards public services. In this study, an artificial intelligence-based method for the evaluation of knowledge is described. This method takes advantage of the structure and contents of the knowledge representation schemas (representing the knowledge of the corresponding experts) to carry out knowledge evaluation. More precisely, the method allows the various comparisons between the schemas to be integrated and the overall schema to evaluate the contribution of each schema.